The electric utility industry is under pressure from all sides: an aging infrastructure, the rapid growth of renewable energy, extreme weather, cybersecurity risks, and a shrinking skilled workforce. Amid these challenges, artificial intelligence (AI) has emerged as a potential game-changer. Yet for many utility leaders, the conversation around AI often oscillates between overhyped promises and outright skepticism.
So, what’s real? What’s practical? And most importantly, what should Strategy & Innovation Leaders prioritize when considering AI for grid modernization?
Let’s separate the hype from the reality.
The Hype: AI Will Revolutionize Utilities Overnight
AI is often portrayed as a silver bullet capable of automating every function within a utility, from predictive asset management to real-time grid control and consumer behaviour forecasting. These headlines promise transformation, but without practical guidance, they risk setting unrealistic expectations.
What many overlook is the complexity of electric utilities: conservative operational cultures, decades-old infrastructure, and strict regulatory requirements. This isn’t a tech startup where you can “move fast and break things.”
The Reality: AI’s Value Lies in Strategic, Targeted Applications
In practice, AI’s greatest value lies in solving specific, high-impact problems, particularly those involving large volumes of data and repeatable patterns. Here’s where it’s already proving its worth:
1. Condition-Based Maintenance
One of the most practical and scalable uses of AI is in optimizing maintenance strategies. Traditional time-based inspections are reactive and inefficient. Instead, utilities can deploy AI-enabled visual and thermal monitoring to shift toward Condition-Based Maintenance (CBM).
Touchless™ Monitoring systems, for example, use fixed sensors to continuously collect thermal and visual data from high-value assets. AI algorithms analyze this data to detect anomalies and trigger alerts before failure occurs. The result? Fewer truck rolls, reduced O&M costs, improved uptime, and a safer working environment for field crews.
Reality Check: AI doesn't replace technicians, it empowers them with better information to make smarter decisions.
2. Asset Performance Management (APM)
Asset Management teams often face pressure to extend the lifespan of aging infrastructure without increasing OPEX. AI tools integrated into APM platforms can help identify underperforming assets, correlate fault histories, and optimize replacement cycles.
When combined with continuous monitoring technologies, AI provides a comprehensive health index for critical infrastructure like transformers, switchgear, and inverters, helping teams justify capital investments and maintain regulatory compliance.
Reality Check: AI models are only as good as the data feeding them. Data quality, standardization, and sensor integration must be addressed up front.
3. Grid Edge Monitoring and Forecasting
As distributed energy resources (DERs) like rooftop solar and EVs proliferate, the grid edge becomes increasingly dynamic. AI is helping utilities forecast demand patterns, manage voltage fluctuations, and predict the impact of DERs on local infrastructure.
AI-driven analytics platforms analyze data from smart meters, substation sensors, and weather feeds to proactively balance the grid and improve power quality.
Reality Check: AI-enhanced forecasting doesn’t eliminate uncertainty, but it does provide faster, more data-informed decisions.
4. Cybersecurity and Anomaly Detection
With the rise of digital substations, IoT devices, and remote monitoring, utilities face escalating cybersecurity threats. AI can bolster defence by continuously analyzing network traffic and identifying suspicious behaviour faster than traditional rules-based systems.
Machine learning algorithms are increasingly being integrated into utility cybersecurity frameworks, offering real-time detection and automated response to potential breaches.
Reality Check: AI is a powerful tool, but it should be part of a broader, layered cybersecurity strategy, not a standalone solution.
How to Evaluate AI Initiatives
AI is not a single product or platform. It’s an enabler that must be aligned with your utility’s strategic objectives. For Strategy & Innovation Leaders, successful deployment hinges on three core pillars:
- Start with the Business Problem
Ask: What operational or strategic challenge are we solving? Whether it's reducing O&M costs, improving reliability, or enhancing worker safety, clarity of purpose drives successful AI implementations. - Data Readiness is Critical
AI needs high-quality, contextualized, and consistent data. Ensure your OT/IT architecture is capable of feeding AI models with reliable inputs from SCADA, GIS, asset management systems, and continuous monitoring solutions. - Focus on Interoperability and Scalability
Choose AI solutions that integrate with your existing ecosystem. Scalable, standards-based platforms reduce the risk of vendor lock-in and make it easier to expand successful pilots across the organization.
Bridging Innovation and Operations
The gap between innovation teams and operations is often where AI projects falter. Innovation leaders must work closely with O&M and asset management teams to co-develop use cases, define measurable KPIs, and ensure that any AI solution fits into existing workflows.
Touchless™ Monitoring solutions are a prime example of this alignment. They deliver immediate value to field crews through real-time thermal alerts, while also supporting long-term strategic goals like grid resilience, predictive maintenance, and CAPEX optimization.
The Time for AI is Now, But Start with Purpose
AI will not transform the utility overnight, but it can meaningfully improve how utilities operate, maintain, and plan their infrastructure. The most successful utilities aren’t the ones chasing the latest AI trends, they’re the ones applying AI with purpose, precision, and a focus on outcomes.
As you assess AI’s role in your innovation roadmap, prioritize applications that drive real operational improvements. Focus on partnerships that understand your industry’s unique challenges. And remember, AI isn’t about replacing people—it’s about giving them the tools to be more effective, efficient, and empowered.